Recent work has tried to incorporate graph information in the AML system in order to capture network patterns. Weber et al.
[1]} benchmarked graph convolutional networks against various supervised methods and concluded that random forest algorithms provide a better performance, despite the lack of graph-based information. Oliveira et al. [2]} propose GuiltyWalker, leveraging random walks on a cryptocurrency graph to characterize distances to previous suspicious activity. The authors reported a 5 p.p. improvement in F1 score when including these novel features. Random walks were also used in [3]} on top of a transaction graph representing the bitcoin network. [4]} propose a community detection approach, from which neighborhood-centric features are extracted and ingested by a supervised machine learning model. On a real-world dataset, the best model was a random forest classifier achieving over 80%recall@20%FPR.
